Is color information only extracted in the first input layer of a convolutional neural network?
Neural networks are all about taking raw input data (RGB values and pixel location) and learning useful features that are relevant to some downstream task. This process of aggregating raw inputs into higher-level features can start at the first layer past the inputs.

So yes, only the first layer of the network is using the actual raw color information from the image. Beyond that, the network has already started to put together nearby pixels and disparate color channels in order to find more complex patterns. Deeper layers in the neural network typically do further aggregation on features learned in earlier layers, rather than taking raw color information as input.